Yield Management Systems (YMSs) are commonly used as computer-based supports to facilitate decisions in several applications, such as in the transportation business and, in particular, in the airline industry. The goal of a yield management system is that of allocating an offered capacity to different types of requests (categories), in order to optimise a yield parameter. For example, the yield management system is used to maximise the revenue that can be obtained from selling a given transportable weight and volume of a cargo flight; the objective is to fly an aircraft as profitable as possible without allowing low-fare freights to displace high-fare freights.
All yield management systems use a forecast method to estimate the requests of the different categories; a decisional system is then applied to the estimated requests for determining a suggestion that should be given to maximise the yield parameter. Typically, stochastic or probabilistic methods are employed, in order to take into account an uncertainty of the estimated requests (and not only their mean value).
A solution known in the art consists of estimating a simple probabilistic distribution of the variables involved in the yield management system. A stochastic linear programming model is then applied using the simple probabilistic distribution to determine an allocation model that maximizes a target function defining the yield parameter. An example of the aforementioned method is described in Williamson, E. L. “Airline Network Seat Inventory Control: Methodologies and Revenue Impact”, Massachusetts Institute of Technology Phd Thesis—Cambridge (Mass) June 1992.
A drawback of the solutions known in the art is that they become computationally intractable when a complexity of the problem defined by the yield management system increases. This is particular critical if the offered capacity is defined by more than one variable, such as the weight and the volume of a cargo flight. In these cases many simplifications, shortcuts and assumptions are introduced, in order to solve the problem in a reasonable amount of time. For example, the number of categories is limited or a manageable form of the probabilistic distributions is assumed (for example a Gauss, Poisson or uniform distribution). Moreover, the known solutions discard some capacity variables (for example using only the weight and just checking a posteriori if volume constraints are satisfied) or discard the stochastic nature of the yield parameter (for example considering only its mean value).
However, all the simplifications described above strongly reduce the accuracy of the yield management system. As a consequence, the solutions known in the art may lead to a relevant economic lost.
It is an object of the present invention to overcome the above-mentioned drawbacks. In order to achieve this object, a method as set out in the first claim is proposed.